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Abstract #2798

Iterative Cross-Domain Deep-Learning Approach for Reconstructing Undersampled Radial MRI

Doohyun Park1, Taejoon Eo1, Taeseong Kim1, Jinseong Jang1, and Dosik Hwang1

1Yonsei University, Seoul, Republic of Korea

The purpose of this study is to eliminate the aliasing artifacts in accerelated radial MRI. We designed a Cross-Domain deep-learning network, called SISI-Net(Sinogram-Image-Sinogram-Image Network). This is an architecture to gradually solves data sparsity problems by iteratively learning the radial sampling data in the sinogram domain and the reconstructed data in the image domain. As a result, proposed network could remove aliasing artifacts effectively while maintaining structural information.

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